Learning about Outgroups: Comparing Deep versus Broad Connections

2024-02-05

2024-07-31

PRODUCTIVITY
Quantity and quality of production (small and big bags) measured each day of the intervention, controlling for lead-in individual productivity

STEREOTYPES / BELIEFS
In a holdout survey with individuals who are not part of the main study, we will measure the behaviors and beliefs of Hindus and Muslims through the following games:
1. (Mis)-reporting the no. of heads in a private coin-toss where each head earns you money (dishonest behavior/ lying)
2.Giving to religious outgroup in a Dictator game (altruism)
3. Movie choice (romantic comedy versus horror) to measure timidness
4. Use of hand sanitizer to measure sanitary practices
5. Lottery game (choice over lottery versus sure payment) to measure risk aversion
6. Self-reported religious versus Indian identity (as well as through choice over bags with religiously distinctive colors versus national flag colors)
7. Self-reported desired number of children (to get at stereotypes around the threat of a growing outgroup population)
8. At the endline, we will incentivize treatment and control participants to correctly predict the behavior of the holdout survey participants to assess how treatment affects generalized belief updating (i.e. updating about people not met) on these dimensions. For some of the questions, we will ask treatment participants to make predictions about their partners at the factory (whereas control participants will also be asked to make predictions about those who worked at the factory for all 9 days) to benchmark effects.
Beyond the outcomes mentioned above, we will also elicit beliefs about the productivity of ingroup and outgroup members after the 3-days of lead in and then again at endline.

SOCIAL PREFERENCES
Dictator giving to outgroup individual

IDENTITY
Self-report whether more attached to being an Indian vs. being a Hindu/Muslim

FRIENDSHIPS/WILLINGNESS TO INTERACT
– Last names of five closest friends (to be coded as Muslim- vs. Hindu-sounding)
– Willingness to work in the future with stranger from outgroup (incentivized)


[NOTE: At the time of submitting this trial registration 14 people had completed the endline survey, but no data had been accessed by any of the research team.]

UPDATE [19th August, 2024]: WHATSAPP MEASURE
More than a month and a half after the intervention concludes, we will send WhatsApp messages to study participants from enumerators associated with the survey firm who have distinctively Hindu and Muslim names. Hindu subjects will receive a message from a Muslim enumerator and Muslim subjects will receive a message from a Hindu enumerator. The messages will inquire about the participants' well-being and that of their families. The outcome be whether or not participants provided a polite response to the message. This will serve as a behavioral measure of civility towards the outgroup.

LABOR MARKET OUTCOMES
– Income earned, hours worked in the last week before endline survey
– Whether factory partners helped get information on or get additional work [TREATMENT INDIVIDUALS ONLY]
– Lent or borrowed money from factory partners [TREATMENT INDIVIDUALS ONLY]

EMOTIONS DURING THE INTERVENTION [TREATMENT INDIVIDUALS ONLY]
– Daily mood surveys to assess emotions (happiness) during production, rate own + partner’s performance, as well as identity fusion with partners

MEMORY [TREATMENT INDIVIDUALS ONLY]
– Recall names of teammates during the intervention
– Overall happiness during the intervention period across all days
-- Number of bags made as a team

POLITICAL ATTITUDES
– Thermometer rating for India’s Prime Minister
– Rankings of political systems

WILLINGNESS TO INTERACT
– Type of work partner preference
– Willingness to accept outgroup as neighbor (self-reported)
– Contact with partners from the factory and whether time spent on various activities (meeting in person, time spent with each other’s families, meals together, chatted via phone or text message)
– Report phone number of partners from the factory
– Interest in attending a social event

We are recruiting Hindus and Muslims aged 18 and above in the city of Barasat (North 24 Parganas) in West Bengal to make paper bags. We have rented a local warehouse for the intervention. Participants must first complete two baseline surveys (one at their home and one at the study site) to be eligible for the study. Afterward, eligible participants will be assigned to work individually on bag production for a 3-day “lead-in” period. Those who complete all 3 lead-in days will then be randomized into the following groups:

(i) A control group who will not receive additional work but will participate in the endline survey
(ii) Deep-Ingroup: Workers will work in pairs for the next 6 days. This group comprises only Hindu workers who will have fixed partners (also Hindus).
(iii) Deep-Outgroup: Workers will work in pairs for the next 6 days. They will have a fixed partner for all 6 days and they will be from the religious outgroup (so all pairs are Hindu-Muslim).
(iv) Broad-Outgroup: Workers will work in pairs for the next 6 days. Their partners will be different each day and they will always be from the religious outgroup (again, all Hindu-Muslim pairs).

Our primary interest is to understand whether different types of exposure (with a fixed outgroup member versus a rotating cast of outgroup members) affect team production, change prejudicial attitudes and behaviors, and help dispel stereotypes. We will also pool across all treatment groups (and compare against the control group) to understand the overall impact of contact in general, and to understand how work affects the formation of social networks and other labor market outcomes.

For our analysis, we will run OLS regressions with randomization strata fixed effects, and a baseline-measured dependent variable when available. We will use robust standard errors when the unit of observation is the individual, and cluster standard errors at the pair-level when the outcome is at that level (such as team production). We will also cluster standard errors at the individual-level when there are multiple observations per individual.

Not available

Randomization will be done wave-by-wave in STATA. The main randomization after the lead-in period to the control group or 6 further days of work (i.e., to the treatment groups (ii), (iii) and (iv) above) will be stratified by religion. Since workers in the broad-outgroup treatment will have rotating partners each day, we will create their schedules using the following algorithm:
1. Consider all possible inter-religion pairs in the broad-outgroup treatment
2. Randomly sort these pairs
3. Randomly draw the number of pairs required to work each day (repeat if an individual appears in more than one team until this is not the case)
4. Repeat 1-3 for the next days with the additional condition that pairs from previous days are not repeated

Individual-level

No

Approx. 1,000 individuals (750 in the three treatments and 250 in control), across roughly 10 production waves.
This target sample size is funding permitting, and one or two additional waves may be possible if additional grants come in. Given this, the expected sample range is 800-1,200.

Approx. 1,000 endline response outcomes (assuming no attrition), and 375 team production outcomes (750 workers always working in teams) for 6 days of the treatment, so in total approximately 2,250 team production outcomes

Roughly equal proportions of each treatment arm (so N=250 for each of the four treatments). However, the randomization and sampling approach will lead to some deviations from equal shares (e.g., if we over-recruit for one wave, that wave will have a higher share of pure control individuals than 25%).